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        <title>Feature Engineering for Second Language Acquisition Modeling</title>
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    <name type="personal">
        <namePart type="given">Guanliang</namePart>
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        <namePart type="given">Claudia</namePart>
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        <namePart type="given">Geert-Jan</namePart>
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            <title>Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications</title>
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        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
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        <name type="personal">
            <namePart type="given">Jill</namePart>
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            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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        <name type="personal">
            <namePart type="given">Claudia</namePart>
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        <name type="personal">
            <namePart type="given">Helen</namePart>
            <namePart type="family">Yannakoudakis</namePart>
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    <abstract>Knowledge tracing serves as a keystone in delivering personalized education. However, few works attempted to model students’ knowledge state in the setting of Second Language Acquisition. The Duolingo Shared Task on Second Language Acquisition Modeling provides students’ trace data that we extensively analyze and engineer features from for the task of predicting whether a student will correctly solve a vocabulary exercise. Our analyses of students’ learning traces reveal that factors like exercise format and engagement impact their exercise performance to a large extent. Overall, we extracted 23 different features as input to a Gradient Tree Boosting framework, which resulted in an AUC score of between 0.80 and 0.82 on the official test set.</abstract>
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    <identifier type="doi">10.18653/v1/W18-0543</identifier>
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%0 Conference Proceedings
%T Feature Engineering for Second Language Acquisition Modeling
%A Chen, Guanliang
%A Hauff, Claudia
%A Houben, Geert-Jan
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F chen-etal-2018-feature
%X Knowledge tracing serves as a keystone in delivering personalized education. However, few works attempted to model students’ knowledge state in the setting of Second Language Acquisition. The Duolingo Shared Task on Second Language Acquisition Modeling provides students’ trace data that we extensively analyze and engineer features from for the task of predicting whether a student will correctly solve a vocabulary exercise. Our analyses of students’ learning traces reveal that factors like exercise format and engagement impact their exercise performance to a large extent. Overall, we extracted 23 different features as input to a Gradient Tree Boosting framework, which resulted in an AUC score of between 0.80 and 0.82 on the official test set.
%R 10.18653/v1/W18-0543
%U https://aclanthology.org/W18-0543
%U https://doi.org/10.18653/v1/W18-0543
%P 356-364

Markdown (Informal)

[Feature Engineering for Second Language Acquisition Modeling](https://aclanthology.org/W18-0543) (Chen et al., BEA 2018)

• Feature Engineering for Second Language Acquisition Modeling (Chen et al., BEA 2018)

ACL

• Guanliang Chen, Claudia Hauff, and Geert-Jan Houben. 2018. Feature Engineering for Second Language Acquisition Modeling. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 356–364, New Orleans, Louisiana. Association for Computational Linguistics.